Statistics for Data Science using Python
- Description
- Curriculum
- FAQ
- Reviews
If You want to be a Data Scientist or Data Analyst then brushing up on your statistics skills is something you need to do.
But it’s just hard to get started with Data Science in most of the course you will find theoritical knowledge on stats not having practical knowledge
I have explained Each topic in a easiest way as well as its implementation in Python from Scratch (most demanding language of Data Science Industry)
That’s exactly why I have created this course for you!
Here you will quickly get the essential stats knowledge for a Data Scientist or Analyst.
I have included real-world use-cases of business challenges to show you how you could apply Stats knowledge to boost your career.
At the same time you can master topics such as Descriptive Stats, distributions, z-test, the Central Limit Theorem, hypothesis testing, & many more!
So what are you waiting for?
Enroll now and & get a transition into Data Science
-
24
Random Variable, Population and Sample Statistics -
25
Types of Statistics
-
26
What are Outliers & Measures of Central Tendancy(Mean,Median,Mode) ?
-
27
Mean,Mode Median implementation using Python
-
28
Measures of Spread (Variance,Standard Dev. ,Range,Inter-Quantile Range)
-
29
Outliers Detection and Removal using Python
-
30
Skewness in Data
-
37
Intro to Probability
-
38
What is Prob. Density Function(PDF) & Cumalative Demnsity Function(CDF) ?
-
39
Bayes Theorem
-
40
What is a Distribution and why we use it?
-
41
Bionomial Distribution
-
42
Binomial Dist. in Python
-
43
Poisson's Distribution
-
44
Poisson Distribution in Python
-
45
Normal Distribution
-
46
Normal Distribution in Python
-
47
Implementation of Z-score(Standarization) in python
-
48
Log-Normal Distribution and Heavy-Tailed Distribution
-
49
Log-Normal and Heavy Tailed Dist.. in Python
-
50
Q-Q Plot
-
51
Central Limit Theorem
-
52
Central Limit Theorem implemntation in Python
-
53
Chebyshew's Inequality
-
54
Estimation Problem based on Z-stats
